{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 5, 7, 9, 20]\n",
    "\n",
    "plt.boxplot(x)\n",
    "plt.show()\n",
    "\n",
    "# 最大值\n",
    "# 3/4 Q3\n",
    "# 2/4 中位数\n",
    "# 1/4 Q1\n",
    "\n",
    "# 异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 5, 7, 9, -10]\n",
    "\n",
    "plt.boxplot(x)\n",
    "plt.show()\n",
    "\n",
    "# 最大值\n",
    "# 3/4 Q3\n",
    "# 2/4 中位数\n",
    "# 1/4 Q1\n",
    "\n",
    "# 异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一次画多个箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.random.randint(10, 100, 100)\n",
    "x2 = np.random.randint(10, 100, 100)\n",
    "x3 = np.random.randint(10, 100, 100)\n",
    "\n",
    "plt.boxplot([x1, x2, x3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_27144\\816020640.py:6: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  plt.boxplot(data, labels=labels, notch=True, sym=\"r*\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.normal(size=(500, 4))\n",
    "data\n",
    "\n",
    "labels = ['A', 'B', 'C', 'D']\n",
    "\n",
    "plt.boxplot(data, labels=labels, notch=True, sym=\"r*\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
